The Concept of Computational Cognitive Modeling - Coursework Example

Computational Cognitive Modeling There is a salient need to understand the human cognitive process. A close analysisof the evolution of cognition reveals that different brain parts play various roles in cognition. Computational cognitive modeling has become one of the approaches used by psychologists to gain an in-depth understanding of human cognition. There are numerous benefits of relying on computational cognitive modeling in simulating cognitive activities. The cognitive models are based on mathematical and computer languages and are of critical importance in evaluating existing cognitive theories. Computational Cognitive Modeling Introduction Psychologists have sought to understand the cognitive processes in detail. There is evidence suggesting that the human brain had different parts that perform different roles. Supporters of evolution highlight that different brain regions have constantly responded to environmental changes. There is a constant need for researchers to understand how different cognitive processes such as perception, language, memory, and thinking in human beings take place. There is scientific evidence suggesting that the brain serves as an information-processing center. The information processed in the brain comes from different sources, especially from the surrounding environment. The brain can filter the needed information, process it adequately, and use it to trigger behavioral change. The field of cognitive psychology has focused on computational modeling to understand how the brain carries cognitive activities such as perception, memory, language, and decision-making. Computational cognitive modeling serves to explore different cognitive processes using computer models. Notably, computer algorithms and programs developed on simulation basis, serve to demonstrate how the brain carries out the different cognitive processes. This paper will discuss computational cognitive modeling, and its contribution to cognitive psychology. Many scientists have been curious to decipher the processes undertaken by the brain in its role to process information. Technological advancements have opened new realms of possibilities, allowing scientist to develop computational models of cognition. There are different types of computational models developed to understand the various cognitive activities. Many computational models seek to address how human performance happens. Therefore, they focus on psychological mechanisms, processes, as well as knowledge structures. There are both qualitative and quantitative computational models, depending on the purpose of the model. Computational cognitive modeling is a concept that emerged in the 1960s, but has undergone rigorous developments in the last few decades. The advanced understanding of neural networks in the brain has led to the establishment of various computational models (Anderson, 2014). Prior to the emergence of computational cognitive modeling, scientists only understood behaviorism on a superficial basis. However, psychologists recognize that the brain carried out complex processes associated with cognition. In addition, there was evidence suggesting that different parameters determined the processes that the brain could carry out. The development of complex processed-based theories served as a precondition for the development of computational models that had the capacity to represent the numerous processes taking place in the brain. An advanced understanding of brain processes allowed for the potential simulation of such processes using computers. Worth noting is the fact that the initial models were only diagrams that comprised of various nodes and directional arrows highlighting the flow of processes in the brain. Modern computational models are highly advanced but playing a similar role. Modern computational models have the capacity to demonstrate the detailed cognitive architecture, giving detail to the different processing levels, as well as the parameters involved in cognitive activities. When the computational model runs on a computer, it simulates the real activities of the brain in its efforts to perform a specific task. The computational models allow scientists to make alterations of certain parameters in an effort to evaluate the existing theories (Cooper, 2002). Computational models provide empirical data that psychologists can use when evaluating different theories. The presence of computational models has allowed scientist to figure out the significance of alternative explanations of different cognitive processes. Studies in the recent past have revealed that there is an evident interplay between the various cognitive faculties of the brain. Therefore, the advancing use of computational models seeks to simulate such interplays in computer programs in order to gain a deeper understanding of the brain processes. Most importantly, computational models have proven to be critical media for carrying out various thought experiments and establishing a new hypothesis. Psychologists are relying on the computational cognitive modeling as the only available avenue for developing new theories for the future. The potential for computational models to give insights into the human mind processes cannot be underestimated. In the recent past, psychologists have been able to understand specific processes involved in the brain cognition by computational cognitive models. The development of computational cognitive models relies on the fact that the brain uses information from the environment and actively processes the information through a series of transformative processes (Heyes & Huber, 2000). Since cognition takes place in different phases, the computational models represent the different phases of cognition. The response developed by the brain serves as the product of all the cognitive processes. The main purpose of advancing computational cognitive models has been to evaluate the different information processing phases. In the past, psychologists developed numerous theories that sought to offer the potential explanation of how different cognitive processes occurred. The theories were developed from experimental evidence. Computational models have the capacity to evaluate the relevance and validity of such theories. Researchers who focus on computational cognitive modeling recognize the value of developing cognitive architectures that can easily conform to the existing psychological theories (Morse & Ziemke, 2008). Cognitive architectures represent a sophisticated programming environment that represents different psychological theories. The cognitive architectures can demonstrate how an identified user can carry out a specific task. There are different levels of computational cognitive modeling. The first level represents computational theories that define the specific tasks, goals, as well as the purpose of all the strategies involved. On the second level, psychologists develop algorithms based on the computational theory developed in the first level. The third level focuses on hardware implementation allowing psychologists to physically represent the algorithms (Psycharis, Botsari, Mantas, & Loukeris, 2014). A close analysis of the modern literature in cognitive science reveals that it relies on computational modeling. Computational models differ from conceptual frameworks as they rely on formal, mathematical and computer languages. As highlighted above, computational cognitive models introduce a new level of analysis of human information processing. These models have the capacity to carry out a mathematical analysis of different cognitive processes. Relying on mathematical analysis makes computational cognitive models effective approaches to developing logically valid predictions. The fact that computational cognitive models have the capacity to make precise quantitative predictions makes them critical tools in modern cognitive psychology. Whereas many conceptual frameworks develop din the past only allowed psychologists to undertake a qualitative analysis of cognitive processes, the computational cognitive models, create the possibilities of analyzing cognitive processes from a quantitative perspective. Computational models also present psychologists with the capacity of generalization. Therefore, a single model can be used in deriving new predictions, especially when psychologists are trying to understand the interplay between different cognitive processes (Sun, 2007). In the modern day, there are numerous practical uses of computational cognitive models. Clinical psychologists rely on such models in an effort to understand the emerging differences in cognitive processes exhibited by clinical patients. Through the use of computational cognitive models, the clinical psychologists can compare the cognitive processes of normal and clinical patients. Neuroscientist have embraced the potential of computational cognitive models in their effort to describe the various psychological functions of the numerous brain areas. There has been a salient need to critically understand the specific role played by each part of the brain. Computational cognitive models have presented a pathway for neuroscientists to define such functions. Other researchers rely on the computational cognitive models when studying the aging process (Benzon & Hays, 2006). There is evidence suggesting that cognitive functioning diminishes with age. For this reason, cognitive models play a critical role in helping scientists understand the age at which an individual will present the highest level of cognitive functioning. On the other hand, aging researchers rely on computational cognitive models when they seek to understand the deterioration of cognitive activities with advancing age. In an era where technology is highly advanced, there is an evident need for researchers to understand how humans can interact with machines. For example, researchers use computational cognitive models to determine the effects of smartphones on the human cognitive processes. The daily interactions of humans and machines such as computers trigger certain changes in the cognitive processes. In order to determine the safety of such machines or to highlight the adverse effects of continued exposure to machines, cognitive models have proved to be important. As mentioned previously, computational cognitive models can play a critical role in decision making because they can be used to make future predictions (Dell, 2008). Many researchers have defined the steps of cognitive modeling. The initial step in computational cognitive modeling is to develop a conceptual theoretical framework or evaluate existing theoretical frameworks. Researchers need to identify the underlying assumptions in the conceptual framework and convert the assumptions into a mathematical language. The conversion of the assumptions to mathematical or computer language relies on basic cognitive principles. In cases whereby the conceptual theory proves inadequate, researchers need to establish additional assumptions that can be used in the development of the model. If researchers need to have valid quantitative predictions, then there is a salient need to develop detailed assumptions. After formulating the assumptions, cognitive psychologist estimates the different parameters associated with the cognitive process under study. Many researchers attach values to each identified parameter using the linear regression model. The final step involves the comparison of the potential predictions, evaluating their capacity to present valid empirical results. Cognitive psychologists must have the expertise of identifying the most effective representation of the human cognitive processes. Worth noting is the fact that cognitive psychologists repeat these steps in an effort to develop the most effective model (Benzon & Hays, 2006). Cognitive psychologists relying on computational cognitive models have appraised the models highlighting that they are easily testable. In addition, the models rely on existing evidence gained from experimental data from other studies. Cognitive modeling through simulation processes has led to the understanding of different cognitive processes in depth. Many psychologists have come to a deeper understanding of various concepts in cognitive psychology through the use of computational models. It is impossible to underestimate the fact that computational cognitive models open up new realms and a broader avenue for future research. Some of the successes of computational cognitive models include the understanding of verb past-tense learning using the connectionist models (Dell, 2008). Many psychologists have sought to understand different aspects of learning languages as exhibited by the brain cognitive processes. The connectionist models served to shed new light on how people learn the past tenses of verbs. Other successes include the Clarion cognitive architecture, which explains both implicit and explicit learning. Finally, other models have been able to demonstrate the significance of tutoring systems. With such success in the past, computational cognitive modeling will play a critical role in the future. There is a salient need for cognitive psychologists to develop simulation models of a diverse range of cognitive activities. In the past, there have been numerous setbacks in the validation of existing theories. Computational cognitive modeling may serve as the best avenue for evaluating such theories in the future. Notably, psychologists are yet to understand various cognitive processes. Therefore, computational cognitive models will be of great significance in the future in recreating such processes and generating empirical data for understanding the processes. In addition, many health conditions cause changes in the cognitive processes. Computational cognitive modeling can help in gaining a deeper understanding of how such changes occur (Benzon & Hays, 2006). Conclusion Without a doubt, understanding the human cognitive processes has been an area of interest for many psychologists. After understanding the evolution of cognition, scientists have sought to gain a deeper understanding of how the brain carries out different cognitive roles. In the past, psychologists have registered remarkable success in developing computational cognitive models that seek to recreate some of the critical brain processes. The development of computational cognitive modeling has not been without challenges. Aspects such as the development of cognitive architectures and the evaluation of cognitive models have been challenging. However, there has been a breakthrough in many of these processes, a factor that highlights the significance of computational modeling in understanding the brain processes that define cognition. There are many issues that require to be addressed in the future. However, there is evidence that computational cognitive modeling is one of the most effective pathways towards gaining a deeper understanding of the brain processes. References Anderson, B. (2014). Computational Neuroscience and Cognitive Modelling : A Students Introduction to Methods and Procedures. Los Angeles: SAGE Publications Ltd. Benzon, W. L., & Hays, D. G. (2006). The Evolution of Cognition. Substance Use & Misuse, 41(14), 1837-1860. Cooper, R. P. (2002). Modelling High-level Cognitive Processes. Mahwah, N.J.: Lawrence Erlbaum Associates, Inc. Dell, G. A. (2008). Introduction to special issue on computational modelling in cognitive neuropsychology. Cognitive Neuropsychology, 25(2), 131-135. Heyes, C. M., & Huber, L. (2000). The Evolution of Cognition. Cambridge, Mass: MIT Press. Morse, A. F., & Ziemke, T. (2008). On the role(s) of modelling in cognitive science. Pragmatics & Cognition, 16(1), 37-56. doi:10.1075/p&c.16.1.04mor Psycharis, S., Botsari, E., Mantas, P., & Loukeris, D. (2014). The impact of the computational inquiry based experiment on metacognitive experiences, modelling indicators and learning performance. Computers & Education, 7290-99. doi:10.1016/j.compedu.2013.10.001 Sun, R. (2007). The importance of cognitive architectures: an analysis based on CLARION. Journal Of Experimental & Theoretical Artificial Intelligence, 19(2), 159-193. Read More